






"""simple docstring"""
import logging

import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed



class        UpperCamelCase__ (				a_):







    """simple docstring"""







    def __init__(		self :	Any				,    UpperCamelCase_ :	List[str]				,    UpperCamelCase_ :	Dict				,    UpperCamelCase_ :	Tuple=1_0_2_4				,    UpperCamelCase_ :	int=1_0_2_4				,    UpperCamelCase_ :	Union[str, Any]=3.6 ):





           '''simple docstring'''

           __magic_name__      				=    tokenizer
           __magic_name__      				=    tokenizer.bos_token_id
           __magic_name__      				=    dataset
           __magic_name__      				=    seq_length
           __magic_name__      				=    seq_length * chars_per_token * num_of_sequences







    def __iter__(		self :	Dict ):





           '''simple docstring'''

           __magic_name__      				=    iter(self.dataset )
           __magic_name__      				=    True
           while more_examples:
                  __magic_name__				,  __magic_name__      				=    [], 0
                  while True:
                         if buffer_len >= self.input_characters:
                                break
                         try:
                                buffer.append(next(UpperCamelCase_ )['content'] )
                                buffer_len += len(buffer[-1] )
                         except StopIteration:
                                __magic_name__      				=    False
                                break
                  __magic_name__      				=    tokenizer(UpperCamelCase_				,    truncation=UpperCamelCase_ )['input_ids']
                  __magic_name__      				=    []
                  for tokenized_input in tokenized_inputs:
                         all_token_ids.extend(tokenized_input + [self.concat_token_id] )
                  for i in range(0				,    len(UpperCamelCase_ )				,    self.seq_length ):
                         __magic_name__      				=    all_token_ids[i : i + self.seq_length]
                         if len(UpperCamelCase_ ) == self.seq_length:
                                yield torch.tensor(UpperCamelCase_ )


def 							A						(				__snake_case: Any  )    ->		Any:

       """simple docstring"""





       __magic_name__      				=    {'streaming': True}
       __magic_name__      				=    load_dataset(args.dataset_name ,     split='train' ,     **__snake_case  )
       __magic_name__      				=    ConstantLengthDataset(__snake_case ,     __snake_case ,     seq_length=args.seq_length  )
       __magic_name__      				=    DataLoader(__snake_case ,     batch_size=args.batch_size  )
       return eval_dataloader


def 							A						(				__snake_case: Union[str, Any]  )    ->		List[Any]:

       """simple docstring"""





       model.eval()
       __magic_name__      				=    []
       for step, batch in enumerate(__snake_case  ):
              with torch.no_grad():
                     __magic_name__      				=    model(__snake_case ,     labels=__snake_case  )
              __magic_name__      				=    outputs.loss.repeat(args.batch_size  )
              losses.append(accelerator.gather(__snake_case  )  )

              if args.max_eval_steps > 0 and step >= args.max_eval_steps:
                     break
       __magic_name__      				=    torch.mean(torch.cat(__snake_case  )  )
       try:
              __magic_name__      				=    torch.exp(__snake_case  )
       except OverflowError:
              __magic_name__      				=    float('inf'  )
       return loss.item(), perplexity.item()


# Setup Accelerator
snake_case : int     		=		Accelerator()

# Parse configuration
snake_case : Optional[Any]     		=		HfArgumentParser(EvaluationArguments)
snake_case : Dict     		=		parser.parse_args()
set_seed(args.seed)

# Logging
snake_case : str     		=		logging.getLogger(__name__)
logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)

# Load model and tokenizer
snake_case : List[str]     		=		AutoModelForCausalLM.from_pretrained(args.model_ckpt)
snake_case : str     		=		AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
snake_case : Tuple     		=		create_dataloader(args)

# Prepare everything with our `accelerator`.
snake_case		,			snake_case : int     		=		accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info("""Evaluating and saving model after training""")
snake_case		,			snake_case : Union[str, Any]     		=		evaluate(args)






logger.info(f"""loss/eval: {eval_loss}, perplexity: {perplexity}""")







"""simple docstring"""
import argparse
import json
import os

from tensorflow.core.protobuf.saved_model_pba import SavedModel


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
snake_case : Optional[Any]     		=		"""."""

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
snake_case : List[str]     		=		[
    """Assert""",
    """AssignVariableOp""",
    """EmptyTensorList""",
    """MergeV2Checkpoints""",
    """ReadVariableOp""",
    """ResourceGather""",
    """RestoreV2""",
    """SaveV2""",
    """ShardedFilename""",
    """StatefulPartitionedCall""",
    """StaticRegexFullMatch""",
    """VarHandleOp""",
]


def 							A						(				__snake_case: Optional[Any] ,     __snake_case: Tuple ,     __snake_case: Any  )    ->		int:

       """simple docstring"""





       __magic_name__      				=    SavedModel()
       __magic_name__      				=    []

       with open(os.path.join(__snake_case ,     'utils' ,     'tf_ops' ,     'onnx.json'  )  ) as f:
              __magic_name__      				=    json.load(__snake_case  )['opsets']

       for i in range(1 ,     opset + 1  ):
              onnx_ops.extend(onnx_opsets[str(__snake_case  )]  )

       with open(__snake_case ,     'rb'  ) as f:
              saved_model.ParseFromString(f.read()  )

       __magic_name__      				=    set()

       # Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
       for meta_graph in saved_model.meta_graphs:
              # Add operations in the graph definition
              model_op_names.update(node.op for node in meta_graph.graph_def.node  )

              # Go through the functions in the graph definition
              for func in meta_graph.graph_def.library.function:
                     # Add operations in each function
                     model_op_names.update(node.op for node in func.node_def  )

    # Convert to list, sorted if you want
       __magic_name__      				=    sorted(__snake_case  )
       __magic_name__      				=    []

       for op in model_op_names:
              if op not in onnx_ops and op not in INTERNAL_OPS:
                     incompatible_ops.append(__snake_case  )

       if strict and len(__snake_case  ) > 0:
              raise Exception(F"""Found the following incompatible ops for the opset {opset}:\n""" + incompatible_ops  )
       elif len(__snake_case  ) > 0:
              print(F"""Found the following incompatible ops for the opset {opset}:"""  )
              print(*__snake_case ,     sep='\n'  )
       else:
              print(F"""The saved model {saved_model_path} can properly be converted with ONNX."""  )


if __name__ == "__main__":
 snake_case : Optional[Any]     		=		argparse.ArgumentParser()
 parser.add_argument("""--saved_model_path""", help="""Path of the saved model to check (the .pb file).""")
 parser.add_argument(
     """--opset""", default=1_2, type=int, help="""The ONNX opset against which the model has to be tested."""
 )
 parser.add_argument(
     """--framework""", choices=["""onnx"""], default="""onnx""", help="""Frameworks against which to test the saved model."""
 )
 parser.add_argument(
     """--strict""", action="""store_true""", help="""Whether make the checking strict (raise errors) or not (raise warnings)"""
 )
 snake_case : Any     		=		parser.parse_args()

 if args.framework == "onnx":
  onnx_compliancy(args.saved_model_path, args.strict, args.opset)